Table 2.

Current dynamics I = g m^Mh^N(V − E) used in antennal lobe neural network model

Cell	Currents	Current dynamics
PN and LN	INa	M = 3; N = 1;
		α1 = 0.32 × (− 37 − V)/(exp((13 − (V + 50))/4) − 1);
		β1 = 0.28 × (V + 10)/(exp(((V + 50) − 40)/5) − 1);
		m∞ = α1/(α1 + β1);
		τm = 1/(α1 + β1);
		α2 = 0.128 × exp((17 − (V + 50))/18);
		β2 = 4/(exp((40 − (V + 50))/5) + 1);
		h∞ = α2/(α2 + β2);
		τh = (α2 + β2)
PN and LN	IK	M = 4; N = 0;
		α1 = 0.032 × (− 35 − V)/(exp((− 35 − V)/5) − 1);
		β1 = 0.5 × exp((− 40 − V)/40);
		m∞ = α1/(α1 + β1);
		τm = 1/(α1 + β1)
PN	IT	M = 2; N = 1;
		m∞ = 1/(1 + exp(−(V + 59)/6.2));
		τm = (1/(exp(−(V + 131.6)/16.7) + exp((V + 16.8)/18.2)) + 0.612)/4.5738;
		h∞ = 1/(1 + exp((V + 83)/4));
		τh = (30.8 + (211.4 + exp((V + 115.2)/5))/(1 + exp((V + 86)/3.2)))/3.7372
PN	IA	M = 4; N = 1;
		m∞ = 1.0/(1 + exp(−(V + 60)/8.5));
		τm = (1.0/( exp((V + 35.82)/19.69) + exp(−(V + 79.69)/12.7)) + 0.37)/3.9482;
		h∞= 1.0/(1 + exp((V + 78)/6));
		If V < −63
		τh = 1.0/((exp((V + 46.05)/5) + exp(−(V + 238.4)/37.45)))/3.9482;
		If V ≥ −63
		τh = 19.0/3.9482
PN	Ih	Voltage dependence (C indicates close state; O indicates open state):
		$C\underset{\beta }{\overset{\alpha }{\leftrightarrow }}O$
		h∞= 1/(1 + exp((V + 75)/5.5));
		τs = (20 + 1000/(exp((V + 71.5)/14.2) + exp(−(V + 89)/11.6)));
		α = h∞/τs
		β = (1 − h∞)/τs
		Calcium dependence (P0 indicates unbound form; P1 indicates bound form; OL implies locked state):
		P0 + 2Ca $\underset{k2}{\overset{k1}{\iff }}$; P1; O + P1 $\underset{k4}{\overset{k3}{\iff }}$ OL;
		k1 = 2.5 × 107 mm−4, k2 = 4 × 10−4 ms−1, k3 = 0.1 ms−1, and k4 = 0.001 ms−1
LN	IT	M = 2; N = 1;
		m∞ = 1/(1 + exp(−(V + 52)/7.4));
		τm = (3 + 1/(exp((V + 27)/10) + exp(−(V + 102)/15)))/6.8986;
		h∞ = 1/(1 + exp((V + 80)/5));
		τh = (85 + 1/(exp((V + 48)/4) + exp(−(V + 407)/50)))/3.7372